code: def simulate_motif_density_localization(
      motif_name,
      seq_length,
      center_size,
      min_motif_counts,
      max_motif_counts,
      num_pos,
      num_neg,
      GC_fraction,
  ):
      """Simulates two classes of seqeuences:
      - Positive class sequences with multiple motif instances in center of the sequence.
      - Negative class sequences with multiple motif instances anywhere in the sequence.
  
      The number of motif instances is uniformly sampled between minimum and maximum motif counts.
  
      Parameters
      ----------
      motif_name : str
          encode motif name
      seq_length : int
          length of sequence
      center_size : int
          length of central part of the sequence where motifs can be positioned
      min_motif_counts : int
          minimum number of motif instances
      max_motif_counts : int
          maximum number of motif instances
      num_pos : int
          number of positive class sequences
      num_neg : int
          number of negative class sequences
      GC_fraction : float
          GC fraction in background sequence
  
      Returns
      -------
      sequence_arr : 1darray
          Contains sequence strings.
      y : 1darray
          Contains labels.
      embedding_arr: 1darray
          Array of embedding objects.
      """
      (
          localized_density_sequence_array,
          positive_embedding_arr,
      ) = motif_density(
          motif_name,
          seq_length,
          num_pos,
          min_motif_counts,
          max_motif_counts,
          GC_fraction,
          center_size,
      )
      (
          unlocalized_density_sequence_array,
          negative_embedding_arr,
      ) = motif_density(
          motif_name,
          seq_length,
          num_neg,
          min_motif_counts,
          max_motif_counts,
          GC_fraction,
      )
      sequence_arr = np.concatenate(
          (
              localized_density_sequence_array,
              unlocalized_density_sequence_array,
          )
      )
      y = np.array([[True]] * num_pos + [[False]] * num_neg)
      embedding_arr = positive_embedding_arr + negative_embedding_arr
      return sequence_arr, y, embedding_arr
  
file_path: deepchem/deepchem/molnet/dnasim.py
imports: import numpy
label_desc: Use image node_id 6 for calling a global function with example usage: simulate_motif_density_localization(motif_name, seq_length, center_size, min_motif_counts, max_motif_counts, num_pos, num_neg, GC_fraction) and returns: sequence_arr,  y,  embedding_arr